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Cross-view localization via redundant sliced observations and a-contrario validation.

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Bibliographic Details
Title:	Cross-view localization via redundant sliced observations and a-contrario validation.
Authors:	Zhang, Yongjun^1,2,3 (AUTHOR), Xiong, Mingtao¹ (AUTHOR), Wan, Yi^1,2,3 (AUTHOR) yi.wan@whu.edu.cn, Xia, Gui-Song⁴ (AUTHOR)
Source:	ISPRS Journal of Photogrammetry & Remote Sensing. Jun2026, Vol. 236, p421-437. 17p.
Subjects:	Geometric rigidity, Wireless geolocation systems, Scientific observation
Abstract:	Cross-view localization (CVL) matches ground-level images with aerial references to determine the geo-position of a camera, enabling smart vehicles to self-localize offline in GNSS-denied environments. However, most CVL methods output only a single observation, the camera pose, and lack the redundant observations required by surveying principles, making it challenging to assess localization reliability through the mutual validation of observational data. To tackle this, we introduce Slice-Loc, a two-stage method featuring an a-contrario reliability validation for CVL. Instead of using the query image as a single input, Slice-Loc divides it into sub-images and estimates the 3-DoF pose for each slice, creating redundant and independent observations. Then, a geometric rigidity formula is proposed to filter out the erroneous 3-DoF poses, and the inliers are merged to generate the final camera pose. Furthermore, we propose a model that quantifies the meaningfulness of localization by estimating the number of false alarms (NFA), according to the distribution of the locations of the sliced images. By eliminating gross errors, Slice-Loc boosts localization accuracy and effectively detects failures. After filtering out mislocalizations, Slice-Loc reduces the proportion of errors exceeding 10 m to under 3%. In cross-city tests on the DReSS-D dataset, Slice-Loc cuts the mean localization error from 4.47 m to 1.86 m and the mean orientation error from 3. 42 ° to 1. 24 ° , outperforming state-of-the-art methods. Code and dataset will be available at: https://github.com/bnothing/Slice-Loc. [ABSTRACT FROM AUTHOR]
	Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Cross-view localization (CVL) matches ground-level images with aerial references to determine the geo-position of a camera, enabling smart vehicles to self-localize offline in GNSS-denied environments. However, most CVL methods output only a single observation, the camera pose, and lack the redundant observations required by surveying principles, making it challenging to assess localization reliability through the mutual validation of observational data. To tackle this, we introduce Slice-Loc, a two-stage method featuring an a-contrario reliability validation for CVL. Instead of using the query image as a single input, Slice-Loc divides it into sub-images and estimates the 3-DoF pose for each slice, creating redundant and independent observations. Then, a geometric rigidity formula is proposed to filter out the erroneous 3-DoF poses, and the inliers are merged to generate the final camera pose. Furthermore, we propose a model that quantifies the meaningfulness of localization by estimating the number of false alarms (NFA), according to the distribution of the locations of the sliced images. By eliminating gross errors, Slice-Loc boosts localization accuracy and effectively detects failures. After filtering out mislocalizations, Slice-Loc reduces the proportion of errors exceeding 10 m to under 3%. In cross-city tests on the DReSS-D dataset, Slice-Loc cuts the mean localization error from 4.47 m to 1.86 m and the mean orientation error from 3. 42 ° to 1. 24 ° , outperforming state-of-the-art methods. Code and dataset will be available at: https://github.com/bnothing/Slice-Loc. [ABSTRACT FROM AUTHOR]
ISSN:	09242716
DOI:	10.1016/j.isprsjprs.2026.04.014